Overview

Dataset statistics

Number of variables23
Number of observations300000
Missing cells1003071
Missing cells (%)14.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory483.3 MiB
Average record size in memory1.6 KiB

Variable types

DateTime3
Categorical7
Text9
Boolean2
Numeric2

Alerts

Company response to consumer is highly overall correlated with Timely response?High correlation
Complaint ID is highly overall correlated with sample_nHigh correlation
Consumer consent provided? is highly overall correlated with Submitted viaHigh correlation
Consumer disputed? is highly overall correlated with sample_nHigh correlation
Submitted via is highly overall correlated with Consumer consent provided?High correlation
Timely response? is highly overall correlated with Company response to consumerHigh correlation
sample_n is highly overall correlated with Complaint ID and 1 other fieldsHigh correlation
Product is highly imbalanced (53.0%)Imbalance
Company public response is highly imbalanced (89.5%)Imbalance
Submitted via is highly imbalanced (87.6%)Imbalance
Company response to consumer is highly imbalanced (54.1%)Imbalance
Timely response? is highly imbalanced (94.2%)Imbalance
Sub-product has 5281 (1.8%) missing valuesMissing
Sub-issue has 19813 (6.6%) missing valuesMissing
Consumer complaint narrative has 217145 (72.4%) missing valuesMissing
Company public response has 143050 (47.7%) missing valuesMissing
Tags has 284017 (94.7%) missing valuesMissing
Consumer consent provided? has 47576 (15.9%) missing valuesMissing
Consumer disputed? has 282792 (94.3%) missing valuesMissing
Complaint ID has unique valuesUnique

Reproduction

Analysis started2026-02-25 20:27:46.767561
Analysis finished2026-02-25 20:28:34.268894
Duration47.5 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct5093
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2011-12-02 00:00:00
Maximum2026-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-25T15:28:34.445232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:34.544102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Product
Categorical

Imbalance 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 MiB
Credit reporting or other personal consumer reports
184545 
Credit reporting, credit repair services, or other personal consumer reports
48507 
Debt collection
22223 
Mortgage
 
9840
Checking or savings account
 
7599
Other values (15)
27286 

Length

Max length76
Median length51
Mean length47.825623
Min length8

Characters and Unicode

Total characters14347687
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank account or service
2nd rowBank account or service
3rd rowBank account or service
4th rowBank account or service
5th rowBank account or service

Common Values

ValueCountFrequency (%)
Credit reporting or other personal consumer reports184545
61.5%
Credit reporting, credit repair services, or other personal consumer reports48507
 
16.2%
Debt collection22223
 
7.4%
Mortgage9840
 
3.3%
Checking or savings account7599
 
2.5%
Credit card6356
 
2.1%
Credit card or prepaid card4629
 
1.5%
Money transfer, virtual currency, or money service3742
 
1.2%
Credit reporting3151
 
1.1%
Student loan2694
 
0.9%
Other values (10)6714
 
2.2%

Length

2026-02-25T15:28:34.652910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credit295863
15.1%
or254338
12.9%
reporting236203
12.0%
personal234317
11.9%
consumer233756
11.9%
other233069
11.9%
reports233052
11.9%
repair48507
 
2.5%
services48507
 
2.5%
debt22391
 
1.1%
Other values (23)124323
6.3%

Most occurring characters

ValueCountFrequency (%)
r2390952
16.7%
e1718154
12.0%
1664326
11.6%
o1505982
10.5%
t1077857
7.5%
s824955
 
5.7%
n781843
 
5.4%
p761768
 
5.3%
i684244
 
4.8%
c433098
 
3.0%
Other values (22)2504508
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)14347687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2390952
16.7%
e1718154
12.0%
1664326
11.6%
o1505982
10.5%
t1077857
7.5%
s824955
 
5.7%
n781843
 
5.4%
p761768
 
5.3%
i684244
 
4.8%
c433098
 
3.0%
Other values (22)2504508
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14347687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2390952
16.7%
e1718154
12.0%
1664326
11.6%
o1505982
10.5%
t1077857
7.5%
s824955
 
5.7%
n781843
 
5.4%
p761768
 
5.3%
i684244
 
4.8%
c433098
 
3.0%
Other values (22)2504508
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14347687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2390952
16.7%
e1718154
12.0%
1664326
11.6%
o1505982
10.5%
t1077857
7.5%
s824955
 
5.7%
n781843
 
5.4%
p761768
 
5.3%
i684244
 
4.8%
c433098
 
3.0%
Other values (22)2504508
17.5%

Sub-product
Text

Missing 

Distinct84
Distinct (%)< 0.1%
Missing5281
Missing (%)1.8%
Memory size21.0 MiB
2026-02-25T15:28:35.003621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length16
Mean length16.994239
Min length4

Characters and Unicode

Total characters5008525
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowChecking account
2nd rowChecking account
3rd rowChecking account
4th rowChecking account
5th rowChecking account
ValueCountFrequency (%)
credit245698
36.7%
reporting231851
34.6%
card21061
 
3.1%
debt11807
 
1.8%
or9844
 
1.5%
other9620
 
1.4%
mortgage9566
 
1.4%
account8186
 
1.2%
i7930
 
1.2%
not7930
 
1.2%
Other values (107)105825
15.8%
2026-02-25T15:28:35.822723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r803558
16.0%
e600266
12.0%
t555822
11.1%
i512183
10.2%
374599
7.5%
o328705
6.6%
n316051
 
6.3%
d299642
 
6.0%
g270135
 
5.4%
p253521
 
5.1%
Other values (43)694043
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)5008525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r803558
16.0%
e600266
12.0%
t555822
11.1%
i512183
10.2%
374599
7.5%
o328705
6.6%
n316051
 
6.3%
d299642
 
6.0%
g270135
 
5.4%
p253521
 
5.1%
Other values (43)694043
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5008525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r803558
16.0%
e600266
12.0%
t555822
11.1%
i512183
10.2%
374599
7.5%
o328705
6.6%
n316051
 
6.3%
d299642
 
6.0%
g270135
 
5.4%
p253521
 
5.1%
Other values (43)694043
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5008525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r803558
16.0%
e600266
12.0%
t555822
11.1%
i512183
10.2%
374599
7.5%
o328705
6.6%
n316051
 
6.3%
d299642
 
6.0%
g270135
 
5.4%
p253521
 
5.1%
Other values (43)694043
13.9%

Issue
Text

Distinct171
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 MiB
2026-02-25T15:28:36.968836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length80
Median length79
Mean length38.656533
Min length4

Characters and Unicode

Total characters11596960
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMaking/receiving payments, sending money
2nd rowProblems caused by my funds being low
3rd rowMaking/receiving payments, sending money
4th rowMaking/receiving payments, sending money
5th rowProblems caused by my funds being low
ValueCountFrequency (%)
your192627
 
11.2%
report187895
 
11.0%
on131554
 
7.7%
information129330
 
7.5%
incorrect129071
 
7.5%
problem104163
 
6.1%
of59175
 
3.5%
improper57881
 
3.4%
use57652
 
3.4%
an55265
 
3.2%
Other values (248)609520
35.6%
2026-02-25T15:28:38.297556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o1449652
12.5%
1414133
12.2%
r1324589
11.4%
n972766
 
8.4%
t937378
 
8.1%
e852766
 
7.4%
i737080
 
6.4%
p451046
 
3.9%
a449022
 
3.9%
c420944
 
3.6%
Other values (39)2587584
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11596960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1449652
12.5%
1414133
12.2%
r1324589
11.4%
n972766
 
8.4%
t937378
 
8.1%
e852766
 
7.4%
i737080
 
6.4%
p451046
 
3.9%
a449022
 
3.9%
c420944
 
3.6%
Other values (39)2587584
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11596960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1449652
12.5%
1414133
12.2%
r1324589
11.4%
n972766
 
8.4%
t937378
 
8.1%
e852766
 
7.4%
i737080
 
6.4%
p451046
 
3.9%
a449022
 
3.9%
c420944
 
3.6%
Other values (39)2587584
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11596960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1449652
12.5%
1414133
12.2%
r1324589
11.4%
n972766
 
8.4%
t937378
 
8.1%
e852766
 
7.4%
i737080
 
6.4%
p451046
 
3.9%
a449022
 
3.9%
c420944
 
3.6%
Other values (39)2587584
22.3%

Sub-issue
Text

Missing 

Distinct264
Distinct (%)0.1%
Missing19813
Missing (%)6.6%
Memory size26.4 MiB
2026-02-25T15:28:38.717804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length145
Median length87
Mean length39.53304
Min length9

Characters and Unicode

Total characters11076644
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowMoney was taken from your account on the wrong day or for the wrong amount
2nd rowOverdrafts and overdraft fees
3rd rowCompany closed your account
4th rowTransaction was not authorized
5th rowProblem using a debit or ATM card
ValueCountFrequency (%)
information118827
 
7.2%
to95302
 
5.7%
your92096
 
5.5%
report87772
 
5.3%
someone84217
 
5.1%
belongs84217
 
5.1%
else84217
 
5.1%
on46589
 
2.8%
investigation46006
 
2.8%
company44218
 
2.7%
Other values (465)876775
52.8%
2026-02-25T15:28:39.959397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1380049
12.5%
o1338733
12.1%
e1058779
 
9.6%
n930268
 
8.4%
r881677
 
8.0%
t841853
 
7.6%
i680834
 
6.1%
s516899
 
4.7%
a408798
 
3.7%
m336385
 
3.0%
Other values (49)2702369
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)11076644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1380049
12.5%
o1338733
12.1%
e1058779
 
9.6%
n930268
 
8.4%
r881677
 
8.0%
t841853
 
7.6%
i680834
 
6.1%
s516899
 
4.7%
a408798
 
3.7%
m336385
 
3.0%
Other values (49)2702369
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11076644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1380049
12.5%
o1338733
12.1%
e1058779
 
9.6%
n930268
 
8.4%
r881677
 
8.0%
t841853
 
7.6%
i680834
 
6.1%
s516899
 
4.7%
a408798
 
3.7%
m336385
 
3.0%
Other values (49)2702369
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11076644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1380049
12.5%
o1338733
12.1%
e1058779
 
9.6%
n930268
 
8.4%
r881677
 
8.0%
t841853
 
7.6%
i680834
 
6.1%
s516899
 
4.7%
a408798
 
3.7%
m336385
 
3.0%
Other values (49)2702369
24.4%
Distinct67337
Distinct (%)81.3%
Missing217145
Missing (%)72.4%
Memory size91.1 MiB
2026-02-25T15:28:41.332761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32520
Median length6856
Mean length1010.7696
Min length10

Characters and Unicode

Total characters83747312
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65163 ?
Unique (%)78.6%

Sample

1st rowMY ACCOUNT WAS TAMPERED WITH FROM WELLS FARGO ACCOUNT ON XX/XX/2015 WITH THE AMOUNT OF {$380.00} TAKEN, I CALLED TO DISPUTE THE TRANSACTION, I SPOKE TO XXXX SPOKE PEOPLE OF WELLS FARGO, THEY STATED THAT MY MONEY WOULD BE PUT BACK INTO MY ACCOUNT WITHIN XXXX DAYS OR LESS AND THAT I WOULD BE CONTACTED BY XXXX AND NEVER WAS CONTACTED, I CALLED TO ASKED AGAIN ABOUT MY ACCOUNT BEING TAMPERED WITH I GET NO ANSWER, I ASKED TO SPEAK TO A SUPERVISOR I WAS DENIED.
2nd rowXXXX Transferred my checking account to BB & T. However they did not send my savings account. In the transition they set me up with a new BB & T account login online, but did not provide me with the information or attach my email. The settings on my checking account were not carried over. XXXX Account : Overdraft protection - Would not allow transactions that would overdraft my account. Would transfer money from savings if an overdraft occurred. BB & T Account : allowed account to continue to go negative, assessed me {$180.00} in overdraft fees in XXXX dayI was not able to go online to monitor my account, I called XXXX times in XXXX days to get assistance with logging in online and was on hold for over XXXX min each time before having to hang up because I was working ( working XXXX hour days for a project ) Was told debits and credits would be transferred between accounts. However when my company attempted to deposit my check it was rejected. As you can imagine I am not a happy customer of either XXXX or BB & T for being so thoughtless to the customer. I wanted to go close my XXXX account on XXXX before the transfer, since I opened a XXXX XXXX account to use, and to my surprise all of the XXXX branches were closed and I had no way to close it. I want all of the debits and anything to stop hitting this account. I want for everysingle overdraft fee to be refunded. I want to pay the balance that was actually paid on my behalf and I want this account closed.
3rd rowThe attached letter to the XXXX of the Bank of America gives an actual account of my recent interaction with the bank. The bank should not be allowed to change the terms of my accounts with them at will and charge me fees at will
4th rowI sold an item on ebay, in my description I specifically stated that i do not accept refunds or exchanges. The buyer reported to ebay that the item was defective so ebay refunded the money to him. The buyer then returned the item to me and I discovered that the item was not defective at all. Ebay and paypal are now trying to bill me {$250.00} dollars for this discrepency. I feel ebay ruled in favor of the buyer without all the information and even false information from the buyer.
5th rowXX/XX/XXXX 2015Federal Bank RegulatorOn XXXX XXXX, 2015 Chase Bank confiscated {$14000.00} from my checking account. They did this with a simple debit entry. They did not provide any advanced notice. This was done without any discovery of fact procedure, arbitration or legal hearing. My account did not have sufficient funds and therefore was overdrawn. A check for local property tax was outstanding and subject to penalties. The entry that triggered this action was a direct deposit from the IRS on XXXX XXXX, 2015, a month and a half earlier. I have not received any correspondence from the IRS about any issues. I understand the clearing of funds between banks and the need to reverse transactions. Chase shows this in funds available for withdrawal. This normally takes days not a month and a half. I believe all issues are with Chase Bank and their handling of the transaction and their procedures for confiscating money. I have contacted Chase and they are working to resolve the error. I hope and expect the monetary portion to be resolved. What can not be resolved is the procedural issue which allows a bank to at their whim without notification and any formal procedure to confiscate money from an account holder. This requires Federal regulation. In summary my question is : Can a bank at any time confiscate money from an account holder without advanced notification and without a formal fact finding hearing? Banking is not practical without assurance of funds. If this is not legal, what is my recourse? XXXX XXXX XXXX XXXX XXXX XXXX, Illinois XXXX XXXX XXXXXXXXXXXX
ValueCountFrequency (%)
xxxx1053283
 
7.2%
the563636
 
3.9%
514677
 
3.5%
to433259
 
3.0%
i388507
 
2.7%
and366840
 
2.5%
my297507
 
2.0%
of291679
 
2.0%
a286203
 
2.0%
that182745
 
1.3%
Other values (55256)10199806
70.0%
2026-02-25T15:28:41.995787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14513503
17.3%
e6992655
 
8.3%
t5708920
 
6.8%
X5098645
 
6.1%
a4666291
 
5.6%
o4401512
 
5.3%
n4380498
 
5.2%
i4264454
 
5.1%
r3719633
 
4.4%
s3112982
 
3.7%
Other values (95)26888219
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)83747312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14513503
17.3%
e6992655
 
8.3%
t5708920
 
6.8%
X5098645
 
6.1%
a4666291
 
5.6%
o4401512
 
5.3%
n4380498
 
5.2%
i4264454
 
5.1%
r3719633
 
4.4%
s3112982
 
3.7%
Other values (95)26888219
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)83747312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14513503
17.3%
e6992655
 
8.3%
t5708920
 
6.8%
X5098645
 
6.1%
a4666291
 
5.6%
o4401512
 
5.3%
n4380498
 
5.2%
i4264454
 
5.1%
r3719633
 
4.4%
s3112982
 
3.7%
Other values (95)26888219
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)83747312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14513503
17.3%
e6992655
 
8.3%
t5708920
 
6.8%
X5098645
 
6.1%
a4666291
 
5.6%
o4401512
 
5.3%
n4380498
 
5.2%
i4264454
 
5.1%
r3719633
 
4.4%
s3112982
 
3.7%
Other values (95)26888219
32.1%

Company public response
Categorical

Imbalance  Missing 

Distinct11
Distinct (%)< 0.1%
Missing143050
Missing (%)47.7%
Memory size31.3 MiB
Company has responded to the consumer and the CFPB and chooses not to provide a public response
149463 
Company believes it acted appropriately as authorized by contract or law
 
4864
Company chooses not to provide a public response
 
1134
Company believes the complaint is the result of a misunderstanding
 
351
Company disputes the facts presented in the complaint
 
339
Other values (6)
 
799

Length

Max length119
Median length95
Mean length93.738662
Min length48

Characters and Unicode

Total characters14712283
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompany chooses not to provide a public response
2nd rowCompany chooses not to provide a public response
3rd rowCompany chooses not to provide a public response
4th rowCompany believes it acted appropriately as authorized by contract or law
5th rowCompany chooses not to provide a public response

Common Values

ValueCountFrequency (%)
Company has responded to the consumer and the CFPB and chooses not to provide a public response149463
49.8%
Company believes it acted appropriately as authorized by contract or law4864
 
1.6%
Company chooses not to provide a public response1134
 
0.4%
Company believes the complaint is the result of a misunderstanding351
 
0.1%
Company disputes the facts presented in the complaint339
 
0.1%
Company believes complaint caused principally by actions of third party outside the control or direction of the company198
 
0.1%
Company believes complaint is the result of an isolated error165
 
0.1%
Company can't verify or dispute the facts in the complaint158
 
0.1%
Company believes the complaint provided an opportunity to answer consumer's questions148
 
< 0.1%
Company believes complaint represents an opportunity for improvement to better serve consumers127
 
< 0.1%
(Missing)143050
47.7%

Length

2026-02-25T15:28:42.093934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the301331
11.5%
to300338
11.5%
and298926
11.4%
company157148
 
6.0%
a150951
 
5.8%
not150597
 
5.7%
public150597
 
5.7%
provide150597
 
5.7%
response150597
 
5.7%
chooses150597
 
5.7%
Other values (52)657966
25.1%

Most occurring characters

ValueCountFrequency (%)
2462695
16.7%
o1534317
10.4%
e1389608
9.4%
s1067043
 
7.3%
n1066932
 
7.3%
a794579
 
5.4%
t787862
 
5.4%
p776871
 
5.3%
d760977
 
5.2%
r629265
 
4.3%
Other values (19)3442134
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)14712283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2462695
16.7%
o1534317
10.4%
e1389608
9.4%
s1067043
 
7.3%
n1066932
 
7.3%
a794579
 
5.4%
t787862
 
5.4%
p776871
 
5.3%
d760977
 
5.2%
r629265
 
4.3%
Other values (19)3442134
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14712283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2462695
16.7%
o1534317
10.4%
e1389608
9.4%
s1067043
 
7.3%
n1066932
 
7.3%
a794579
 
5.4%
t787862
 
5.4%
p776871
 
5.3%
d760977
 
5.2%
r629265
 
4.3%
Other values (19)3442134
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14712283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2462695
16.7%
o1534317
10.4%
e1389608
9.4%
s1067043
 
7.3%
n1066932
 
7.3%
a794579
 
5.4%
t787862
 
5.4%
p776871
 
5.3%
d760977
 
5.2%
r629265
 
4.3%
Other values (19)3442134
23.4%

Company
Text

Distinct2737
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size24.1 MiB
2026-02-25T15:28:42.568134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length78
Mean length27.32877
Min length3

Characters and Unicode

Total characters8198631
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1044 ?
Unique (%)0.3%

Sample

1st rowBANK OF AMERICA, NATIONAL ASSOCIATION
2nd rowTCF NATIONAL BANK
3rd rowWELLS FARGO & COMPANY
4th rowSynovus Bank
5th rowPNC Bank N.A.
ValueCountFrequency (%)
inc246494
24.6%
holdings82233
 
8.2%
equifax78158
 
7.8%
intermediate77540
 
7.7%
transunion77325
 
7.7%
solutions71920
 
7.2%
information70492
 
7.0%
experian70037
 
7.0%
financial12440
 
1.2%
llc12345
 
1.2%
Other values (2716)204373
20.4%
2026-02-25T15:28:43.354406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I776543
 
9.5%
703345
 
8.6%
N640177
 
7.8%
E427870
 
5.2%
n416172
 
5.1%
A350958
 
4.3%
o327203
 
4.0%
S280162
 
3.4%
T272818
 
3.3%
i264142
 
3.2%
Other values (69)3739241
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8198631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I776543
 
9.5%
703345
 
8.6%
N640177
 
7.8%
E427870
 
5.2%
n416172
 
5.1%
A350958
 
4.3%
o327203
 
4.0%
S280162
 
3.4%
T272818
 
3.3%
i264142
 
3.2%
Other values (69)3739241
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8198631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I776543
 
9.5%
703345
 
8.6%
N640177
 
7.8%
E427870
 
5.2%
n416172
 
5.1%
A350958
 
4.3%
o327203
 
4.0%
S280162
 
3.4%
T272818
 
3.3%
i264142
 
3.2%
Other values (69)3739241
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8198631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I776543
 
9.5%
703345
 
8.6%
N640177
 
7.8%
E427870
 
5.2%
n416172
 
5.1%
A350958
 
4.3%
o327203
 
4.0%
S280162
 
3.4%
T272818
 
3.3%
i264142
 
3.2%
Other values (69)3739241
45.6%

State
Text

Distinct62
Distinct (%)< 0.1%
Missing1350
Missing (%)0.4%
Memory size16.8 MiB
2026-02-25T15:28:44.072944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length2
Mean length2.0030738
Min length2

Characters and Unicode

Total characters598218
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowND
2nd rowMN
3rd rowMN
4th rowOH
5th rowPA
ValueCountFrequency (%)
fl40617
13.6%
tx38906
13.0%
ca30645
 
10.3%
ga22097
 
7.4%
ny18168
 
6.1%
il12945
 
4.3%
pa12162
 
4.1%
nc10206
 
3.4%
nj9929
 
3.3%
md7604
 
2.5%
Other values (56)95479
32.0%
2026-02-25T15:28:44.871054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%
Distinct16584
Distinct (%)5.5%
Missing697
Missing (%)0.2%
Memory size17.7 MiB
2026-02-25T15:28:45.647681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1496515
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4320 ?
Unique (%)1.4%

Sample

1st row58503
2nd row55125
3rd row55110
4th row44108
5th row18944
ValueCountFrequency (%)
xxxxx4887
 
1.6%
30349593
 
0.2%
33025459
 
0.2%
77449447
 
0.1%
33311410
 
0.1%
19143400
 
0.1%
30253393
 
0.1%
60411391
 
0.1%
30331385
 
0.1%
30318376
 
0.1%
Other values (16574)290562
97.1%
2026-02-25T15:28:46.244118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0213883
14.3%
3206103
13.8%
1181500
12.1%
2159731
10.7%
7142771
9.5%
4119333
8.0%
6105617
7.1%
9103528
6.9%
5102468
6.8%
896525
6.4%
Other values (2)65056
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1496515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0213883
14.3%
3206103
13.8%
1181500
12.1%
2159731
10.7%
7142771
9.5%
4119333
8.0%
6105617
7.1%
9103528
6.9%
5102468
6.8%
896525
6.4%
Other values (2)65056
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1496515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0213883
14.3%
3206103
13.8%
1181500
12.1%
2159731
10.7%
7142771
9.5%
4119333
8.0%
6105617
7.1%
9103528
6.9%
5102468
6.8%
896525
6.4%
Other values (2)65056
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1496515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0213883
14.3%
3206103
13.8%
1181500
12.1%
2159731
10.7%
7142771
9.5%
4119333
8.0%
6105617
7.1%
9103528
6.9%
5102468
6.8%
896525
6.4%
Other values (2)65056
 
4.3%

Tags
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing284017
Missing (%)94.7%
Memory size18.4 MiB
Servicemember
10109 
Older American
4709 
Older American, Servicemember
1165 

Length

Max length29
Median length13
Mean length14.460865
Min length13

Characters and Unicode

Total characters231128
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOlder American
2nd rowOlder American
3rd rowOlder American
4th rowOlder American, Servicemember
5th rowOlder American

Common Values

ValueCountFrequency (%)
Servicemember10109
 
3.4%
Older American4709
 
1.6%
Older American, Servicemember1165
 
0.4%
(Missing)284017
94.7%

Length

2026-02-25T15:28:46.376256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-25T15:28:46.512414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
servicemember11274
49.0%
older5874
25.5%
american5874
25.5%

Most occurring characters

ValueCountFrequency (%)
e56844
24.6%
r34296
14.8%
m28422
12.3%
i17148
 
7.4%
c17148
 
7.4%
S11274
 
4.9%
v11274
 
4.9%
b11274
 
4.9%
7039
 
3.0%
O5874
 
2.5%
Other values (6)30535
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)231128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e56844
24.6%
r34296
14.8%
m28422
12.3%
i17148
 
7.4%
c17148
 
7.4%
S11274
 
4.9%
v11274
 
4.9%
b11274
 
4.9%
7039
 
3.0%
O5874
 
2.5%
Other values (6)30535
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)231128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e56844
24.6%
r34296
14.8%
m28422
12.3%
i17148
 
7.4%
c17148
 
7.4%
S11274
 
4.9%
v11274
 
4.9%
b11274
 
4.9%
7039
 
3.0%
O5874
 
2.5%
Other values (6)30535
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)231128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e56844
24.6%
r34296
14.8%
m28422
12.3%
i17148
 
7.4%
c17148
 
7.4%
S11274
 
4.9%
v11274
 
4.9%
b11274
 
4.9%
7039
 
3.0%
O5874
 
2.5%
Other values (6)30535
13.2%

Consumer consent provided?
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing47576
Missing (%)15.9%
Memory size21.0 MiB
Consent not provided
161428 
Consent provided
82895 
Other
 
7738
Consent withdrawn
 
363

Length

Max length20
Median length20
Mean length18.222281
Min length5

Characters and Unicode

Total characters4599741
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsent provided
2nd rowConsent provided
3rd rowConsent provided
4th rowConsent not provided
5th rowConsent not provided

Common Values

ValueCountFrequency (%)
Consent not provided161428
53.8%
Consent provided82895
27.6%
Other7738
 
2.6%
Consent withdrawn363
 
0.1%
(Missing)47576
 
15.9%

Length

2026-02-25T15:28:46.776479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-25T15:28:46.910676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consent244686
37.2%
provided244323
37.1%
not161428
24.5%
other7738
 
1.2%
withdrawn363
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n651163
14.2%
o650437
14.1%
e496747
10.8%
d489009
10.6%
t414215
9.0%
406114
8.8%
r252424
 
5.5%
C244686
 
5.3%
i244686
 
5.3%
s244686
 
5.3%
Other values (6)505574
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4599741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n651163
14.2%
o650437
14.1%
e496747
10.8%
d489009
10.6%
t414215
9.0%
406114
8.8%
r252424
 
5.5%
C244686
 
5.3%
i244686
 
5.3%
s244686
 
5.3%
Other values (6)505574
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4599741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n651163
14.2%
o650437
14.1%
e496747
10.8%
d489009
10.6%
t414215
9.0%
406114
8.8%
r252424
 
5.5%
C244686
 
5.3%
i244686
 
5.3%
s244686
 
5.3%
Other values (6)505574
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4599741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n651163
14.2%
o650437
14.1%
e496747
10.8%
d489009
10.6%
t414215
9.0%
406114
8.8%
r252424
 
5.5%
C244686
 
5.3%
i244686
 
5.3%
s244686
 
5.3%
Other values (6)505574
11.0%

Submitted via
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
Web
286179 
Referral
 
6077
Phone
 
4748
Postal mail
 
2395
Fax
 
554
Other values (2)
 
47

Length

Max length12
Median length3
Mean length3.1980033
Min length3

Characters and Unicode

Total characters959401
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowReferral
5th rowWeb

Common Values

ValueCountFrequency (%)
Web286179
95.4%
Referral6077
 
2.0%
Phone4748
 
1.6%
Postal mail2395
 
0.8%
Fax554
 
0.2%
Web Referral38
 
< 0.1%
Email9
 
< 0.1%

Length

2026-02-25T15:28:47.033140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-25T15:28:47.124480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
web286217
94.6%
referral6115
 
2.0%
phone4748
 
1.6%
postal2395
 
0.8%
mail2395
 
0.8%
fax554
 
0.2%
email9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e303195
31.6%
W286217
29.8%
b286217
29.8%
r12230
 
1.3%
a11468
 
1.2%
l10914
 
1.1%
o7143
 
0.7%
P7143
 
0.7%
R6115
 
0.6%
f6115
 
0.6%
Other values (10)22644
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)959401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e303195
31.6%
W286217
29.8%
b286217
29.8%
r12230
 
1.3%
a11468
 
1.2%
l10914
 
1.1%
o7143
 
0.7%
P7143
 
0.7%
R6115
 
0.6%
f6115
 
0.6%
Other values (10)22644
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)959401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e303195
31.6%
W286217
29.8%
b286217
29.8%
r12230
 
1.3%
a11468
 
1.2%
l10914
 
1.1%
o7143
 
0.7%
P7143
 
0.7%
R6115
 
0.6%
f6115
 
0.6%
Other values (10)22644
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)959401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e303195
31.6%
W286217
29.8%
b286217
29.8%
r12230
 
1.3%
a11468
 
1.2%
l10914
 
1.1%
o7143
 
0.7%
P7143
 
0.7%
R6115
 
0.6%
f6115
 
0.6%
Other values (10)22644
 
2.4%
Distinct4965
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2011-12-02 00:00:00
Maximum2026-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-25T15:28:47.350655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:47.554326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Company response to consumer
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
Closed with explanation
172179 
Closed with non-monetary relief
103041 
In progress
18955 
Closed with monetary relief
 
4386
Untimely response
 
516
Other values (3)
 
923

Length

Max length31
Median length23
Mean length25.009967
Min length6

Characters and Unicode

Total characters7502990
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed with relief
2nd rowClosed with relief
3rd rowClosed without relief
4th rowClosed without relief
5th rowClosed without relief

Common Values

ValueCountFrequency (%)
Closed with explanation172179
57.4%
Closed with non-monetary relief103041
34.3%
In progress18955
 
6.3%
Closed with monetary relief4386
 
1.5%
Untimely response516
 
0.2%
Closed408
 
0.1%
Closed without relief395
 
0.1%
Closed with relief120
 
< 0.1%

Length

2026-02-25T15:28:47.787519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-25T15:28:48.017490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
closed280529
28.4%
with279726
28.3%
explanation172179
17.4%
relief107942
 
10.9%
non-monetary103041
 
10.4%
in18955
 
1.9%
progress18955
 
1.9%
monetary4386
 
0.4%
untimely516
 
0.1%
response516
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e796522
10.6%
687140
 
9.2%
o683042
 
9.1%
n677854
 
9.0%
l561166
 
7.5%
i560758
 
7.5%
t560638
 
7.5%
a451785
 
6.0%
s319471
 
4.3%
C280529
 
3.7%
Other values (14)1924085
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)7502990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e796522
10.6%
687140
 
9.2%
o683042
 
9.1%
n677854
 
9.0%
l561166
 
7.5%
i560758
 
7.5%
t560638
 
7.5%
a451785
 
6.0%
s319471
 
4.3%
C280529
 
3.7%
Other values (14)1924085
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7502990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e796522
10.6%
687140
 
9.2%
o683042
 
9.1%
n677854
 
9.0%
l561166
 
7.5%
i560758
 
7.5%
t560638
 
7.5%
a451785
 
6.0%
s319471
 
4.3%
C280529
 
3.7%
Other values (14)1924085
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7502990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e796522
10.6%
687140
 
9.2%
o683042
 
9.1%
n677854
 
9.0%
l561166
 
7.5%
i560758
 
7.5%
t560638
 
7.5%
a451785
 
6.0%
s319471
 
4.3%
C280529
 
3.7%
Other values (14)1924085
25.6%

Timely response?
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.1 KiB
True
297971 
False
 
2029
ValueCountFrequency (%)
True297971
99.3%
False2029
 
0.7%
2026-02-25T15:28:48.243134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Consumer disputed?
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing282792
Missing (%)94.3%
Memory size586.1 KiB
False
 
13862
True
 
3346
(Missing)
282792 
ValueCountFrequency (%)
False13862
 
4.6%
True3346
 
1.1%
(Missing)282792
94.3%
2026-02-25T15:28:48.296885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Complaint ID
Real number (ℝ)

High correlation  Unique 

Distinct300000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10521736
Minimum795
Maximum19151293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2026-02-25T15:28:48.404469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum795
5-th percentile2201591.2
Q16566231.8
median10687577
Q314853852
95-th percentile18248574
Maximum19151293
Range19150498
Interquartile range (IQR)8287620.5

Descriptive statistics

Standard deviation5076875.8
Coefficient of variation (CV)0.48251313
Kurtosis-1.0318129
Mean10521736
Median Absolute Deviation (MAD)4144043
Skewness-0.13851804
Sum3.1565209 × 1012
Variance2.5774668 × 1013
MonotonicityNot monotonic
2026-02-25T15:28:48.509815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350521
 
< 0.1%
375731
 
< 0.1%
397931
 
< 0.1%
345711
 
< 0.1%
370471
 
< 0.1%
322211
 
< 0.1%
177891
 
< 0.1%
299201
 
< 0.1%
360601
 
< 0.1%
377621
 
< 0.1%
Other values (299990)299990
> 99.9%
ValueCountFrequency (%)
7951
< 0.1%
11101
< 0.1%
15451
< 0.1%
17131
< 0.1%
19111
< 0.1%
24541
< 0.1%
25431
< 0.1%
26401
< 0.1%
27781
< 0.1%
28301
< 0.1%
ValueCountFrequency (%)
191512931
< 0.1%
191510941
< 0.1%
191509201
< 0.1%
191508671
< 0.1%
191506391
< 0.1%
191503751
< 0.1%
191501311
< 0.1%
191497041
< 0.1%
191494991
< 0.1%
191493131
< 0.1%
Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2011-10-01 00:00:00
Maximum2026-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-25T15:28:48.618574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:48.714813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

geo
Text

Distinct62
Distinct (%)< 0.1%
Missing1350
Missing (%)0.4%
Memory size16.8 MiB
2026-02-25T15:28:49.028287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length2
Mean length2.0030738
Min length2

Characters and Unicode

Total characters598218
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowND
2nd rowMN
3rd rowMN
4th rowOH
5th rowPA
ValueCountFrequency (%)
fl40617
13.6%
tx38906
13.0%
ca30645
 
10.3%
ga22097
 
7.4%
ny18168
 
6.1%
il12945
 
4.3%
pa12162
 
4.1%
nc10206
 
3.4%
nj9929
 
3.3%
md7604
 
2.5%
Other values (56)95479
32.0%
2026-02-25T15:28:49.378540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)598218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A98656
16.5%
L66771
11.2%
N54083
9.0%
C52778
8.8%
T47333
 
7.9%
F40618
 
6.8%
X38906
 
6.5%
M28517
 
4.8%
I27712
 
4.6%
G22134
 
3.7%
Other values (15)120710
20.2%

region
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
South
161601 
West
48610 
Northeast
47833 
Midwest
39340 
Other
 
2616

Length

Max length9
Median length5
Mean length5.7380067
Min length4

Characters and Unicode

Total characters1721402
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMidwest
2nd rowMidwest
3rd rowMidwest
4th rowMidwest
5th rowNortheast

Common Values

ValueCountFrequency (%)
South161601
53.9%
West48610
 
16.2%
Northeast47833
 
15.9%
Midwest39340
 
13.1%
Other2616
 
0.9%

Length

2026-02-25T15:28:49.471504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-25T15:28:49.539771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
south161601
53.9%
west48610
 
16.2%
northeast47833
 
15.9%
midwest39340
 
13.1%
other2616
 
0.9%

Most occurring characters

ValueCountFrequency (%)
t347833
20.2%
h212050
12.3%
o209434
12.2%
S161601
9.4%
u161601
9.4%
e138399
 
8.0%
s135783
 
7.9%
r50449
 
2.9%
W48610
 
2.8%
N47833
 
2.8%
Other values (6)207809
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1721402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t347833
20.2%
h212050
12.3%
o209434
12.2%
S161601
9.4%
u161601
9.4%
e138399
 
8.0%
s135783
 
7.9%
r50449
 
2.9%
W48610
 
2.8%
N47833
 
2.8%
Other values (6)207809
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1721402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t347833
20.2%
h212050
12.3%
o209434
12.2%
S161601
9.4%
u161601
9.4%
e138399
 
8.0%
s135783
 
7.9%
r50449
 
2.9%
W48610
 
2.8%
N47833
 
2.8%
Other values (6)207809
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1721402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t347833
20.2%
h212050
12.3%
o209434
12.2%
S161601
9.4%
u161601
9.4%
e138399
 
8.0%
s135783
 
7.9%
r50449
 
2.9%
W48610
 
2.8%
N47833
 
2.8%
Other values (6)207809
12.1%

stratum
Text

Distinct2543
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size33.9 MiB
2026-02-25T15:28:49.850179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length93
Median length89
Mean length61.56363
Min length20

Characters and Unicode

Total characters18469089
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)0.1%

Sample

1st rowBank account or service|2012Q1|Midwest
2nd rowBank account or service|2012Q1|Midwest
3rd rowBank account or service|2012Q1|Midwest
4th rowBank account or service|2012Q1|Midwest
5th rowBank account or service|2012Q1|Northeast
ValueCountFrequency (%)
credit295863
15.1%
or254338
12.9%
personal234317
11.9%
consumer233756
11.9%
other233069
11.9%
reporting233052
11.9%
repair48507
 
2.5%
services48507
 
2.5%
debt22391
 
1.1%
reports|2025q4|south20563
 
1.0%
Other values (2123)339963
17.3%
2026-02-25T15:28:51.328224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r2441401
13.2%
e1856553
 
10.1%
o1715416
 
9.3%
1664326
 
9.0%
t1425690
 
7.7%
s960738
 
5.2%
n781843
 
4.2%
p761768
 
4.1%
i723584
 
3.9%
2652642
 
3.5%
Other values (37)5485128
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)18469089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2441401
13.2%
e1856553
 
10.1%
o1715416
 
9.3%
1664326
 
9.0%
t1425690
 
7.7%
s960738
 
5.2%
n781843
 
4.2%
p761768
 
4.1%
i723584
 
3.9%
2652642
 
3.5%
Other values (37)5485128
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18469089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2441401
13.2%
e1856553
 
10.1%
o1715416
 
9.3%
1664326
 
9.0%
t1425690
 
7.7%
s960738
 
5.2%
n781843
 
4.2%
p761768
 
4.1%
i723584
 
3.9%
2652642
 
3.5%
Other values (37)5485128
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18469089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2441401
13.2%
e1856553
 
10.1%
o1715416
 
9.3%
1664326
 
9.0%
t1425690
 
7.7%
s960738
 
5.2%
n781843
 
4.2%
p761768
 
4.1%
i723584
 
3.9%
2652642
 
3.5%
Other values (37)5485128
29.7%

sample_n
Real number (ℝ)

High correlation 

Distinct288
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5606.7287
Minimum1
Maximum20563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2026-02-25T15:28:51.459789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1385
median3034
Q39649
95-th percentile20563
Maximum20563
Range20562
Interquartile range (IQR)9264

Descriptive statistics

Standard deviation6547.0623
Coefficient of variation (CV)1.1677152
Kurtosis-0.078173138
Mean5606.7287
Median Absolute Deviation (MAD)2898
Skewness1.1417225
Sum1.6820186 × 109
Variance42864025
MonotonicityNot monotonic
2026-02-25T15:28:51.679684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2056320563
 
6.9%
1758617586
 
5.9%
1442214422
 
4.8%
1284012840
 
4.3%
96499649
 
3.2%
80478047
 
2.7%
67506750
 
2.2%
62996299
 
2.1%
48224822
 
1.6%
47564756
 
1.6%
Other values (278)194266
64.8%
ValueCountFrequency (%)
1225
 
0.1%
2354
0.1%
3372
0.1%
4380
0.1%
5575
0.2%
6672
0.2%
7546
0.2%
8416
0.1%
9576
0.2%
10460
0.2%
ValueCountFrequency (%)
2056320563
6.9%
1758617586
5.9%
1442214422
4.8%
1284012840
4.3%
96499649
3.2%
80478047
 
2.7%
67506750
 
2.2%
62996299
 
2.1%
48224822
 
1.6%
47564756
 
1.6%

Interactions

2026-02-25T15:28:29.444914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:28.824776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:29.721711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-25T15:28:29.202057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-25T15:28:51.957362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Company public responseCompany response to consumerComplaint IDConsumer consent provided?Consumer disputed?ProductSubmitted viaTagsTimely response?regionsample_n
Company public response1.0000.0970.2600.0690.0940.2240.0660.1070.1490.0210.090
Company response to consumer0.0971.0000.2980.0810.1080.2020.0880.1240.5070.0370.169
Complaint ID0.2600.2981.0000.1620.0290.3710.1620.1860.0760.0630.700
Consumer consent provided?0.0690.0810.1621.0000.0440.1831.0000.1000.0410.0250.157
Consumer disputed?0.0940.1080.0290.0441.0000.0490.0740.0000.0360.0071.000
Product0.2240.2020.3710.1830.0491.0000.1900.2870.1560.0740.287
Submitted via0.0660.0880.1621.0000.0740.1901.0000.2060.0310.0770.094
Tags0.1070.1240.1860.1000.0000.2870.2061.0000.0110.1230.170
Timely response?0.1490.5070.0760.0410.0360.1560.0310.0111.0000.0180.079
region0.0210.0370.0630.0250.0070.0740.0770.1230.0181.0000.348
sample_n0.0900.1690.7000.1571.0000.2870.0940.1700.0790.3481.000

Missing values

2026-02-25T15:28:30.286473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-25T15:28:31.216487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-25T15:28:33.266535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Date receivedProductSub-productIssueSub-issueConsumer complaint narrativeCompany public responseCompanyStateZIP codeTagsConsumer consent provided?Submitted viaDate sent to companyCompany response to consumerTimely response?Consumer disputed?Complaint IDyear_quartergeoregionstratumsample_n
02012-03-14Bank account or serviceChecking accountMaking/receiving payments, sending moneyNoneNoneNoneBANK OF AMERICA, NATIONAL ASSOCIATIONND58503NoneNoneWeb2012-03-15Closed with reliefYesNo350522012Q1NDMidwestBank account or service|2012Q1|Midwest4
12012-03-20Bank account or serviceChecking accountProblems caused by my funds being lowNoneNoneNoneTCF NATIONAL BANKMN55125NoneNoneWeb2012-03-21Closed with reliefYesNo375732012Q1MNMidwestBank account or service|2012Q1|Midwest4
22012-03-22Bank account or serviceChecking accountMaking/receiving payments, sending moneyNoneNoneNoneWELLS FARGO & COMPANYMN55110NoneNoneWeb2012-03-23Closed without reliefYesYes397932012Q1MNMidwestBank account or service|2012Q1|Midwest4
32012-03-07Bank account or serviceChecking accountMaking/receiving payments, sending moneyNoneNoneNoneSynovus BankOH44108NoneNoneReferral2012-03-16Closed without reliefYesNo345712012Q1OHMidwestBank account or service|2012Q1|Midwest4
42012-03-20Bank account or serviceChecking accountProblems caused by my funds being lowNoneNoneNonePNC Bank N.A.PA18944Older AmericanNoneWeb2012-03-23Closed without reliefYesYes370472012Q1PANortheastBank account or service|2012Q1|Northeast6
52012-03-05Bank account or serviceOther bank product/serviceDeposits and withdrawalsNoneNoneNoneCITIZENS FINANCIAL GROUP, INC.PA16601NoneNoneReferral2012-03-08Closed with reliefYesNo322212012Q1PANortheastBank account or service|2012Q1|Northeast6
62012-03-21Bank account or serviceSavings accountDeposits and withdrawalsNoneNoneNoneCITIBANK, N.A.NY11434NoneNoneReferral2012-03-22Closed with reliefYesNo177892012Q1NYNortheastBank account or service|2012Q1|Northeast6
72012-03-05Bank account or serviceChecking accountProblems caused by my funds being lowNoneNoneNoneJPMORGAN CHASE & CO.NY10118Older AmericanNoneWeb2012-03-05Closed with reliefYesNo299202012Q1NYNortheastBank account or service|2012Q1|Northeast6
82012-03-16Bank account or serviceChecking accountProblems caused by my funds being lowNoneNoneNoneASTORIA BANKNY11786NoneNoneWeb2012-03-19Closed without reliefYesYes360602012Q1NYNortheastBank account or service|2012Q1|Northeast6
92012-03-21Bank account or serviceChecking accountAccount opening, closing, or managementNoneNoneNoneTD BANK US HOLDING COMPANYPA19053NoneNonePhone2012-03-22Closed without reliefYesYes377622012Q1PANortheastBank account or service|2012Q1|Northeast6
Date receivedProductSub-productIssueSub-issueConsumer complaint narrativeCompany public responseCompanyStateZIP codeTagsConsumer consent provided?Submitted viaDate sent to companyCompany response to consumerTimely response?Consumer disputed?Complaint IDyear_quartergeoregionstratumsample_n
2999902026-01-12Vehicle loan or leaseLoanGetting a loan or leaseChanges in terms mid-deal or after closingNoneNoneExeter Finance, LLC.GA30314NoneNoneWeb2026-01-12In progressYesNone186581392026Q1GASouthVehicle loan or lease|2026Q1|South20
2999912026-01-10Vehicle loan or leaseLoanGetting a loan or leaseChanges in terms mid-deal or after closingNoneNoneSANTANDER HOLDINGS USA, INC.FL33905NoneNoneWeb2026-01-10Closed with explanationYesNone186208142026Q1FLSouthVehicle loan or lease|2026Q1|South20
2999922026-01-13Vehicle loan or leaseLeaseProblems at the end of the loan or leaseUnable to receive car title or other problem after the loan is paid offNoneNoneSANTANDER HOLDINGS USA, INC.FL33602NoneNoneWeb2026-01-13Closed with explanationYesNone186816602026Q1FLSouthVehicle loan or lease|2026Q1|South20
2999932026-01-02Vehicle loan or leaseLoanManaging the loan or leaseBilling problemNoneNoneALLY FINANCIAL INC.MT59701NoneOtherWeb2026-01-02Closed with explanationYesNone184338022026Q1MTWestVehicle loan or lease|2026Q1|West7
2999942026-01-08Vehicle loan or leaseLoanManaging the loan or leaseProblem with additional products or services purchased with the loanNoneNoneBayside CreditCAXXXXXNoneNoneWeb2026-01-08Untimely responseNoNone185580652026Q1CAWestVehicle loan or lease|2026Q1|West7
2999952026-01-08Vehicle loan or leaseLoanRepossessionCompany explaining amount owedNoneNoneSANTANDER HOLDINGS USA, INC.CA91792ServicememberNoneWeb2026-01-08Closed with explanationYesNone185612662026Q1CAWestVehicle loan or lease|2026Q1|West7
2999962026-01-13Vehicle loan or leaseLoanManaging the loan or leaseBilling problemNoneNoneTOYOTA MOTOR CREDIT CORPORATIONCA92301NoneNonePhone2026-01-13In progressYesNone186931372026Q1CAWestVehicle loan or lease|2026Q1|West7
2999972026-01-17Vehicle loan or leaseLoanManaging the loan or leaseBilling problemNoneNoneWestlake Services, LLCNV89014NoneNoneWeb2026-01-17In progressYesNone188064112026Q1NVWestVehicle loan or lease|2026Q1|West7
2999982026-01-05Vehicle loan or leaseLeaseProblem with a company's investigation into an existing problemTheir investigation did not fix an error on your reportNoneNoneBANK OF AMERICA, NATIONAL ASSOCIATIONCA91731NoneNoneWeb2026-01-05In progressYesNone184606512026Q1CAWestVehicle loan or lease|2026Q1|West7
2999992026-01-19Vehicle loan or leaseLoanManaging the loan or leaseBilling problemNoneNoneDriveway Finance CorporationMT599XXNoneNoneWeb2026-01-19Closed with explanationYesNone188353862026Q1MTWestVehicle loan or lease|2026Q1|West7